Overview

Dataset statistics

Number of variables15
Number of observations342
Missing cells1190
Missing cells (%)23.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.6 KiB
Average record size in memory121.6 B

Variable types

DateTime1
Categorical1
Numeric13

Alerts

PTL_total is highly overall correlated with PTL_MDU (Business Days Mean) and 12 other fieldsHigh correlation
PTL_MDU (Business Days Mean) is highly overall correlated with PTL_total and 12 other fieldsHigh correlation
PTL_MSD (Saturdays Mean) is highly overall correlated with PTL_total and 12 other fieldsHigh correlation
PTL_MDO (Sundays Mean) is highly overall correlated with PTL_total and 12 other fieldsHigh correlation
PTL_MAX (Daily Max) is highly overall correlated with PTL_total and 12 other fieldsHigh correlation
stations is highly overall correlated with PTL_total and 12 other fieldsHigh correlation
total_dpea is highly overall correlated with PTL_total and 12 other fieldsHigh correlation
interval is highly overall correlated with PTL_total and 12 other fieldsHigh correlation
PEL_total is highly overall correlated with PTL_total and 11 other fieldsHigh correlation
PEL_business_day_mean is highly overall correlated with PTL_total and 12 other fieldsHigh correlation
PEL_saturday_mean is highly overall correlated with PTL_total and 12 other fieldsHigh correlation
PEL_sunday_mean is highly overall correlated with PTL_total and 12 other fieldsHigh correlation
PEL_max is highly overall correlated with PTL_total and 12 other fieldsHigh correlation
line is highly overall correlated with PTL_total and 11 other fieldsHigh correlation
PTL_MSD (Saturdays Mean) has 5 (1.5%) missing valuesMissing
stations has 174 (50.9%) missing valuesMissing
total_dpea has 174 (50.9%) missing valuesMissing
interval has 230 (67.3%) missing valuesMissing
PEL_total has 119 (34.8%) missing valuesMissing
PEL_business_day_mean has 119 (34.8%) missing valuesMissing
PEL_saturday_mean has 119 (34.8%) missing valuesMissing
PEL_sunday_mean has 119 (34.8%) missing valuesMissing
PEL_max has 119 (34.8%) missing valuesMissing
line is uniformly distributedUniform

Reproduction

Analysis started2023-06-10 23:54:07.153865
Analysis finished2023-06-10 23:54:24.232550
Duration17.08 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

date
Date

Distinct57
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Minimum2018-08-01 00:00:00
Maximum2023-04-01 00:00:00
2023-06-10T20:54:24.319398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:24.431677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

line
Categorical

HIGH CORRELATION  UNIFORM 

Distinct6
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
1
57 
15
57 
2
57 
3
57 
4
57 

Length

Max length2
Median length1
Mean length1.1666667
Min length1

Characters and Unicode

Total characters399
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row15
4th row2
5th row5

Common Values

ValueCountFrequency (%)
1 57
16.7%
15 57
16.7%
2 57
16.7%
3 57
16.7%
4 57
16.7%
5 57
16.7%

Length

2023-06-10T20:54:24.546029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-10T20:54:24.654862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 57
16.7%
15 57
16.7%
2 57
16.7%
3 57
16.7%
4 57
16.7%
5 57
16.7%

Most occurring characters

ValueCountFrequency (%)
1 114
28.6%
5 114
28.6%
2 57
14.3%
3 57
14.3%
4 57
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 399
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 114
28.6%
5 114
28.6%
2 57
14.3%
3 57
14.3%
4 57
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 399
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 114
28.6%
5 114
28.6%
2 57
14.3%
3 57
14.3%
4 57
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 399
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 114
28.6%
5 114
28.6%
2 57
14.3%
3 57
14.3%
4 57
14.3%

PTL_total
Real number (ℝ)

Distinct334
Distinct (%)98.5%
Missing3
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean15117870
Minimum398000
Maximum39719000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:24.767239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum398000
5-th percentile1574600
Q18197000
median13941000
Q319975500
95-th percentile35373900
Maximum39719000
Range39321000
Interquartile range (IQR)11778500

Descriptive statistics

Standard deviation9976524.9
Coefficient of variation (CV)0.65991604
Kurtosis-0.22931809
Mean15117870
Median Absolute Deviation (MAD)5899000
Skewness0.6280489
Sum5.1249579 × 109
Variance9.953105 × 1013
MonotonicityNot monotonic
2023-06-10T20:54:24.894416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8889000 2
 
0.6%
35360000 2
 
0.6%
2576000 2
 
0.6%
2519000 2
 
0.6%
8844000 2
 
0.6%
19118000 1
 
0.3%
2090000 1
 
0.3%
10956070 1
 
0.3%
2066000 1
 
0.3%
21708000 1
 
0.3%
Other values (324) 324
94.7%
(Missing) 3
 
0.9%
ValueCountFrequency (%)
398000 1
0.3%
445000 1
0.3%
498000 1
0.3%
516000 1
0.3%
663000 1
0.3%
710000 1
0.3%
903000 1
0.3%
1086000 1
0.3%
1161000 1
0.3%
1333000 1
0.3%
ValueCountFrequency (%)
39719000 1
0.3%
38994000 1
0.3%
38705000 1
0.3%
38679000 1
0.3%
38308000 1
0.3%
38229000 1
0.3%
38145000 1
0.3%
37779000 1
0.3%
37775000 1
0.3%
37571000 1
0.3%
Distinct293
Distinct (%)86.4%
Missing3
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean606098.53
Minimum22000
Maximum1538000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:25.021705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum22000
5-th percentile64900
Q1331500
median553000
Q3809500
95-th percentile1447700
Maximum1538000
Range1516000
Interquartile range (IQR)478000

Descriptive statistics

Standard deviation398042.32
Coefficient of variation (CV)0.65672873
Kurtosis-0.23864419
Mean606098.53
Median Absolute Deviation (MAD)244000
Skewness0.62441348
Sum2.054674 × 108
Variance1.5843769 × 1011
MonotonicityNot monotonic
2023-06-10T20:54:25.139135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
649000 3
 
0.9%
818000 3
 
0.9%
64000 3
 
0.9%
548000 3
 
0.9%
1477000 3
 
0.9%
68000 2
 
0.6%
469000 2
 
0.6%
99000 2
 
0.6%
648000 2
 
0.6%
535000 2
 
0.6%
Other values (283) 314
91.8%
(Missing) 3
 
0.9%
ValueCountFrequency (%)
22000 2
0.6%
23000 2
0.6%
27000 1
0.3%
32000 1
0.3%
37000 1
0.3%
44000 1
0.3%
48000 1
0.3%
52000 1
0.3%
57000 1
0.3%
58000 1
0.3%
ValueCountFrequency (%)
1538000 1
 
0.3%
1525000 1
 
0.3%
1519000 1
 
0.3%
1497000 1
 
0.3%
1493000 1
 
0.3%
1481000 1
 
0.3%
1477000 3
0.9%
1470000 1
 
0.3%
1466000 1
 
0.3%
1463000 1
 
0.3%

PTL_MSD (Saturdays Mean)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct283
Distinct (%)84.0%
Missing5
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean357180.39
Minimum3000
Maximum933000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:25.260664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3000
5-th percentile32800
Q1196000
median309000
Q3482000
95-th percentile856000
Maximum933000
Range930000
Interquartile range (IQR)286000

Descriptive statistics

Standard deviation246731.65
Coefficient of variation (CV)0.69077604
Kurtosis-0.38539999
Mean357180.39
Median Absolute Deviation (MAD)139000
Skewness0.69172277
Sum1.2036979 × 108
Variance6.0876508 × 1010
MonotonicityNot monotonic
2023-06-10T20:54:25.374051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
217000 3
 
0.9%
222000 3
 
0.9%
42000 3
 
0.9%
196000 3
 
0.9%
299000 3
 
0.9%
208000 3
 
0.9%
39000 3
 
0.9%
31000 2
 
0.6%
623000 2
 
0.6%
231000 2
 
0.6%
Other values (273) 310
90.6%
(Missing) 5
 
1.5%
ValueCountFrequency (%)
3000 1
0.3%
6000 1
0.3%
14000 1
0.3%
15000 1
0.3%
17000 1
0.3%
21000 1
0.3%
23000 1
0.3%
25000 2
0.6%
26000 1
0.3%
27000 1
0.3%
ValueCountFrequency (%)
933000 1
0.3%
924000 1
0.3%
914000 1
0.3%
909000 1
0.3%
893000 1
0.3%
892000 1
0.3%
890000 2
0.6%
887000 1
0.3%
884000 1
0.3%
873000 1
0.3%

PTL_MDO (Sundays Mean)
Real number (ℝ)

Distinct240
Distinct (%)70.8%
Missing3
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean203710.21
Minimum2000
Maximum573000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:25.494995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile18000
Q1104500
median179000
Q3264500
95-th percentile495000
Maximum573000
Range571000
Interquartile range (IQR)160000

Descriptive statistics

Standard deviation143959.33
Coefficient of variation (CV)0.7066869
Kurtosis-0.27455524
Mean203710.21
Median Absolute Deviation (MAD)78000
Skewness0.72028294
Sum69057760
Variance2.072429 × 1010
MonotonicityNot monotonic
2023-06-10T20:54:25.607743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21000 5
 
1.5%
20000 5
 
1.5%
34000 4
 
1.2%
114000 4
 
1.2%
23000 3
 
0.9%
163000 3
 
0.9%
233000 3
 
0.9%
9000 3
 
0.9%
18000 3
 
0.9%
24000 3
 
0.9%
Other values (230) 303
88.6%
ValueCountFrequency (%)
2000 2
 
0.6%
4000 2
 
0.6%
9000 3
0.9%
11000 1
 
0.3%
13000 1
 
0.3%
14000 2
 
0.6%
16000 2
 
0.6%
17000 2
 
0.6%
18000 3
0.9%
20000 5
1.5%
ValueCountFrequency (%)
573000 1
0.3%
557000 1
0.3%
556000 1
0.3%
543000 1
0.3%
538000 1
0.3%
537000 1
0.3%
528000 1
0.3%
526000 1
0.3%
523000 1
0.3%
520000 1
0.3%

PTL_MAX (Daily Max)
Real number (ℝ)

Distinct301
Distinct (%)88.8%
Missing3
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean650882.51
Minimum23000
Maximum1611000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:25.733729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum23000
5-th percentile69900
Q1359500
median607000
Q3851000
95-th percentile1508300
Maximum1611000
Range1588000
Interquartile range (IQR)491500

Descriptive statistics

Standard deviation421148.31
Coefficient of variation (CV)0.64704199
Kurtosis-0.34340512
Mean650882.51
Median Absolute Deviation (MAD)246000
Skewness0.56670231
Sum2.2064917 × 108
Variance1.773659 × 1011
MonotonicityNot monotonic
2023-06-10T20:54:25.848013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
373000 3
 
0.9%
385000 3
 
0.9%
1483000 2
 
0.6%
76000 2
 
0.6%
853000 2
 
0.6%
1513000 2
 
0.6%
591000 2
 
0.6%
114000 2
 
0.6%
801000 2
 
0.6%
1048000 2
 
0.6%
Other values (291) 317
92.7%
(Missing) 3
 
0.9%
ValueCountFrequency (%)
23000 2
0.6%
25000 2
0.6%
37000 1
0.3%
39000 1
0.3%
46000 1
0.3%
49000 1
0.3%
54000 1
0.3%
57000 1
0.3%
63000 2
0.6%
66000 2
0.6%
ValueCountFrequency (%)
1611000 1
0.3%
1585000 1
0.3%
1571000 1
0.3%
1558000 1
0.3%
1552000 1
0.3%
1550000 1
0.3%
1548000 1
0.3%
1544000 2
0.6%
1539000 1
0.3%
1532000 1
0.3%

stations
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)3.6%
Missing174
Missing (%)50.9%
Infinite0
Infinite (%)0.0%
Mean15.571429
Minimum10
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:25.951710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q111
median15.5
Q318
95-th percentile23
Maximum23
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3562488
Coefficient of variation (CV)0.2797591
Kurtosis-0.99644197
Mean15.571429
Median Absolute Deviation (MAD)3.5
Skewness0.3858772
Sum2616
Variance18.976903
MonotonicityNot monotonic
2023-06-10T20:54:26.038936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
11 40
 
11.7%
18 28
 
8.2%
14 28
 
8.2%
23 28
 
8.2%
17 28
 
8.2%
10 16
 
4.7%
(Missing) 174
50.9%
ValueCountFrequency (%)
10 16
 
4.7%
11 40
11.7%
14 28
8.2%
17 28
8.2%
18 28
8.2%
23 28
8.2%
ValueCountFrequency (%)
23 28
8.2%
18 28
8.2%
17 28
8.2%
14 28
8.2%
11 40
11.7%
10 16
 
4.7%

total_dpea
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct156
Distinct (%)92.9%
Missing174
Missing (%)50.9%
Infinite0
Infinite (%)0.0%
Mean539427.8
Minimum56000
Maximum1092000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:26.141647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum56000
5-th percentile81750
Q1350750
median515470
Q3727000
95-th percentile1036650
Maximum1092000
Range1036000
Interquartile range (IQR)376250

Descriptive statistics

Standard deviation295292.95
Coefficient of variation (CV)0.54741887
Kurtosis-0.82293366
Mean539427.8
Median Absolute Deviation (MAD)177000
Skewness0.14722387
Sum90623870
Variance8.7197929 × 1010
MonotonicityNot monotonic
2023-06-10T20:54:26.264958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1010000 2
 
0.6%
64000 2
 
0.6%
99000 2
 
0.6%
617000 2
 
0.6%
549000 2
 
0.6%
288000 2
 
0.6%
298000 2
 
0.6%
108000 2
 
0.6%
112000 2
 
0.6%
1064000 2
 
0.6%
Other values (146) 148
43.3%
(Missing) 174
50.9%
ValueCountFrequency (%)
56000 1
0.3%
58000 1
0.3%
64000 2
0.6%
65000 1
0.3%
68000 1
0.3%
74000 1
0.3%
78000 1
0.3%
80000 1
0.3%
85000 1
0.3%
87000 1
0.3%
ValueCountFrequency (%)
1092000 1
0.3%
1086000 1
0.3%
1066000 1
0.3%
1064000 2
0.6%
1060000 1
0.3%
1058000 1
0.3%
1052000 1
0.3%
1037000 1
0.3%
1036000 1
0.3%
1026000 1
0.3%

interval
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)26.8%
Missing230
Missing (%)67.3%
Infinite0
Infinite (%)0.0%
Mean147.125
Minimum119
Maximum235
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:26.395263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile119
Q1119
median131.5
Q3176.5
95-th percentile210
Maximum235
Range116
Interquartile range (IQR)57.5

Descriptive statistics

Standard deviation34.012484
Coefficient of variation (CV)0.23118086
Kurtosis-0.58763508
Mean147.125
Median Absolute Deviation (MAD)12.5
Skewness0.92809199
Sum16478
Variance1156.8491
MonotonicityNot monotonic
2023-06-10T20:54:26.509747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
119 31
 
9.1%
120 8
 
2.3%
196 8
 
2.3%
133 8
 
2.3%
174 7
 
2.0%
184 7
 
2.0%
123 6
 
1.8%
210 4
 
1.2%
134 3
 
0.9%
136 3
 
0.9%
Other values (20) 27
 
7.9%
(Missing) 230
67.3%
ValueCountFrequency (%)
119 31
9.1%
120 8
 
2.3%
121 1
 
0.3%
123 6
 
1.8%
124 2
 
0.6%
125 2
 
0.6%
126 1
 
0.3%
127 2
 
0.6%
128 2
 
0.6%
130 1
 
0.3%
ValueCountFrequency (%)
235 2
 
0.6%
217 2
 
0.6%
210 4
1.2%
206 1
 
0.3%
202 1
 
0.3%
196 8
2.3%
192 1
 
0.3%
188 2
 
0.6%
184 7
2.0%
174 7
2.0%

PEL_total
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct223
Distinct (%)100.0%
Missing119
Missing (%)34.8%
Infinite0
Infinite (%)0.0%
Mean33296767
Minimum219000
Maximum9.7 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:26.622396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum219000
5-th percentile858600
Q15072500
median12946000
Q320123000
95-th percentile30184500
Maximum9.7 × 108
Range9.69781 × 108
Interquartile range (IQR)15050500

Descriptive statistics

Standard deviation1.3470726 × 108
Coefficient of variation (CV)4.045656
Kurtosis40.74117
Mean33296767
Median Absolute Deviation (MAD)7562000
Skewness6.4820801
Sum7.425179 × 109
Variance1.8146047 × 1016
MonotonicityNot monotonic
2023-06-10T20:54:26.753883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32413000 1
 
0.3%
16326000 1
 
0.3%
7899000 1
 
0.3%
14620000 1
 
0.3%
17307000 1
 
0.3%
8167000 1
 
0.3%
1214000 1
 
0.3%
8506000 1
 
0.3%
15384000 1
 
0.3%
18109000 1
 
0.3%
Other values (213) 213
62.3%
(Missing) 119
34.8%
ValueCountFrequency (%)
219000 1
0.3%
243000 1
0.3%
325000 1
0.3%
397000 1
0.3%
405000 1
0.3%
504000 1
0.3%
545000 1
0.3%
606000 1
0.3%
709000 1
0.3%
767000 1
0.3%
ValueCountFrequency (%)
970000000 1
0.3%
966000000 1
0.3%
932000000 1
0.3%
887000000 1
0.3%
831000000 1
0.3%
32413000 1
0.3%
32289000 1
0.3%
31728000 1
0.3%
31717000 1
0.3%
31037000 1
0.3%

PEL_business_day_mean
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct196
Distinct (%)87.9%
Missing119
Missing (%)34.8%
Infinite0
Infinite (%)0.0%
Mean509852.02
Minimum13000
Maximum1241000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:26.870704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13000
5-th percentile36000
Q1132500
median509000
Q3752000
95-th percentile1165400
Maximum1241000
Range1228000
Interquartile range (IQR)619500

Descriptive statistics

Standard deviation365266.96
Coefficient of variation (CV)0.71641761
Kurtosis-0.94982454
Mean509852.02
Median Absolute Deviation (MAD)284000
Skewness0.28746419
Sum1.13697 × 108
Variance1.3341996 × 1011
MonotonicityNot monotonic
2023-06-10T20:54:26.990265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000 3
 
0.9%
64000 3
 
0.9%
62000 3
 
0.9%
493000 2
 
0.6%
647000 2
 
0.6%
69000 2
 
0.6%
36000 2
 
0.6%
614000 2
 
0.6%
46000 2
 
0.6%
51000 2
 
0.6%
Other values (186) 200
58.5%
(Missing) 119
34.8%
ValueCountFrequency (%)
13000 2
0.6%
16000 1
0.3%
18000 1
0.3%
20000 1
0.3%
27000 2
0.6%
31000 1
0.3%
34000 1
0.3%
35000 2
0.6%
36000 2
0.6%
38000 1
0.3%
ValueCountFrequency (%)
1241000 1
0.3%
1227000 1
0.3%
1223000 1
0.3%
1222000 1
0.3%
1221000 1
0.3%
1216000 1
0.3%
1212000 2
0.6%
1211000 1
0.3%
1187000 1
0.3%
1177000 1
0.3%

PEL_saturday_mean
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct183
Distinct (%)82.1%
Missing119
Missing (%)34.8%
Infinite0
Infinite (%)0.0%
Mean302529.15
Minimum1000
Maximum761000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:27.105949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile15200
Q199500
median263000
Q3487500
95-th percentile697500
Maximum761000
Range760000
Interquartile range (IQR)388000

Descriptive statistics

Standard deviation224005.71
Coefficient of variation (CV)0.74044339
Kurtosis-1.0965712
Mean302529.15
Median Absolute Deviation (MAD)212000
Skewness0.29633313
Sum67464000
Variance5.0178557 × 1010
MonotonicityNot monotonic
2023-06-10T20:54:27.684707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26000 4
 
1.2%
23000 3
 
0.9%
19000 3
 
0.9%
34000 3
 
0.9%
36000 3
 
0.9%
724000 2
 
0.6%
729000 2
 
0.6%
231000 2
 
0.6%
18000 2
 
0.6%
511000 2
 
0.6%
Other values (173) 197
57.6%
(Missing) 119
34.8%
ValueCountFrequency (%)
1000 1
0.3%
3000 1
0.3%
7000 2
0.6%
8000 1
0.3%
10000 1
0.3%
12000 2
0.6%
13000 1
0.3%
14000 1
0.3%
15000 2
0.6%
17000 2
0.6%
ValueCountFrequency (%)
761000 1
0.3%
753000 1
0.3%
729000 2
0.6%
724000 2
0.6%
722000 1
0.3%
712000 1
0.3%
704000 1
0.3%
700000 1
0.3%
699000 1
0.3%
698000 1
0.3%

PEL_sunday_mean
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct157
Distinct (%)70.4%
Missing119
Missing (%)34.8%
Infinite0
Infinite (%)0.0%
Mean173547.09
Minimum1000
Maximum466000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:27.807057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile9100
Q154000
median161000
Q3276500
95-th percentile402000
Maximum466000
Range465000
Interquartile range (IQR)222500

Descriptive statistics

Standard deviation128735.52
Coefficient of variation (CV)0.74179017
Kurtosis-0.92373264
Mean173547.09
Median Absolute Deviation (MAD)108000
Skewness0.34669862
Sum38701000
Variance1.6572834 × 1010
MonotonicityNot monotonic
2023-06-10T20:54:27.920463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 7
 
2.0%
14000 5
 
1.5%
13000 4
 
1.2%
20000 4
 
1.2%
12000 4
 
1.2%
153000 3
 
0.9%
18000 3
 
0.9%
235000 3
 
0.9%
8000 3
 
0.9%
321000 3
 
0.9%
Other values (147) 184
53.8%
(Missing) 119
34.8%
ValueCountFrequency (%)
1000 1
 
0.3%
2000 1
 
0.3%
4000 1
 
0.3%
5000 2
 
0.6%
6000 1
 
0.3%
7000 1
 
0.3%
8000 3
0.9%
9000 2
 
0.6%
10000 7
2.0%
11000 3
0.9%
ValueCountFrequency (%)
466000 1
0.3%
461000 1
0.3%
451000 1
0.3%
431000 1
0.3%
429000 1
0.3%
425000 2
0.6%
418000 1
0.3%
412000 1
0.3%
410000 1
0.3%
409000 1
0.3%

PEL_max
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct186
Distinct (%)83.4%
Missing119
Missing (%)34.8%
Infinite0
Infinite (%)0.0%
Mean555816.14
Minimum13000
Maximum1307000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-06-10T20:54:28.029063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13000
5-th percentile40100
Q1191500
median565000
Q3828000
95-th percentile1233900
Maximum1307000
Range1294000
Interquartile range (IQR)636500

Descriptive statistics

Standard deviation390923.78
Coefficient of variation (CV)0.7033329
Kurtosis-1.0392436
Mean555816.14
Median Absolute Deviation (MAD)310000
Skewness0.22193679
Sum1.23947 × 108
Variance1.528214 × 1011
MonotonicityNot monotonic
2023-06-10T20:54:28.152966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43000 4
 
1.2%
55000 4
 
1.2%
74000 4
 
1.2%
64000 3
 
0.9%
286000 3
 
0.9%
46000 3
 
0.9%
1113000 2
 
0.6%
888000 2
 
0.6%
37000 2
 
0.6%
66000 2
 
0.6%
Other values (176) 194
56.7%
(Missing) 119
34.8%
ValueCountFrequency (%)
13000 1
0.3%
14000 1
0.3%
21000 1
0.3%
23000 1
0.3%
25000 1
0.3%
29000 1
0.3%
30000 1
0.3%
34000 1
0.3%
37000 2
0.6%
39000 1
0.3%
ValueCountFrequency (%)
1307000 1
0.3%
1298000 1
0.3%
1293000 1
0.3%
1267000 1
0.3%
1261000 2
0.6%
1257000 1
0.3%
1256000 1
0.3%
1254000 1
0.3%
1248000 1
0.3%
1241000 1
0.3%

Interactions

2023-06-10T20:54:22.428506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:07.423409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:08.962921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:10.196179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:11.369838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:12.560840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:13.817092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.917765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:16.030157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:17.576423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:18.765942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:19.971759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:21.237215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:22.537803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:07.831788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:09.070628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:10.295971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:11.472699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:12.669162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:13.900376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.997620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:16.126404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:17.676293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:18.868827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:20.072611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:21.333978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:22.640076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:07.937259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:09.176416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:10.393885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:11.575510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:12.774575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:13.983853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:15.076287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:16.215091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:17.776170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:18.970589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:20.174971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:21.431751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:22.724325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:08.030356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:09.267767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:10.474724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:11.662235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:12.866312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.081739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:15.171028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:16.318420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:17.859046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:19.055004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:20.261692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:21.512416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:22.815614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:08.127111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:09.367188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:10.564077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:11.755135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:12.963911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.165033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:15.251417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:16.406117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:17.950344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:19.148823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:20.356021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:21.601279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:22.916253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:08.232960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:09.473113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:10.661981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:11.855993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:13.069393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.251549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:15.336018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:16.878248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:18.050305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:19.249067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:20.457851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:21.696049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:23.001023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:08.320306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:09.559907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:10.756337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:11.942741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:13.158273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.336211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:15.424301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:16.965999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:18.134441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:19.329842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:20.543233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:21.782868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:23.081697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:08.400483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:09.640282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:10.843686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:12.024000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:13.241511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.418497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:15.504519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:17.050744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:18.218187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:19.414984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:20.637502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:21.870245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:23.168303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:08.483290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:09.725012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:10.935687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:12.111228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:13.331716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.502288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:15.590879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:17.139636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:18.303882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:19.506292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:20.728747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:21.963445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:23.257014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:08.576277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:09.819882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:11.022572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:12.201014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:13.437960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.586077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:15.676100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:17.226374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:18.391732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:19.599047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:20.825952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:22.055201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:23.347688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:08.675509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:09.915750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:11.110841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:12.293205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:13.530539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.664505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:15.754832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:17.308064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:18.485118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:19.690811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:20.919301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:22.153935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:23.441367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:08.779813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:10.014062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:11.202728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:12.386218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:13.633900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.748234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:15.839524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:17.399391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:18.586484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:19.785649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:21.016116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:22.256143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:23.526067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:08.869688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:10.102363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:11.282951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:12.470557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:13.722242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:14.837665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:15.930735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:17.491685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:18.671193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:19.872988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:21.132466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-10T20:54:22.338800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-10T20:54:28.261746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
PTL_totalPTL_MDU (Business Days Mean)PTL_MSD (Saturdays Mean)PTL_MDO (Sundays Mean)PTL_MAX (Daily Max)stationstotal_dpeaintervalPEL_totalPEL_business_day_meanPEL_saturday_meanPEL_sunday_meanPEL_maxline
PTL_total1.0000.9960.9840.9810.9890.7280.994-0.9260.8590.9510.9810.9810.9700.532
PTL_MDU (Business Days Mean)0.9961.0000.9810.9800.9920.7301.000-0.9310.8570.9550.9770.9810.9730.534
PTL_MSD (Saturdays Mean)0.9840.9811.0000.9860.9710.7190.982-0.9200.8540.9390.9960.9860.9620.575
PTL_MDO (Sundays Mean)0.9810.9800.9861.0000.9730.6950.980-0.9040.8570.9440.9910.9950.9680.571
PTL_MAX (Daily Max)0.9890.9920.9710.9731.0000.7360.992-0.9220.8480.9500.9710.9780.9800.551
stations0.7280.7300.7190.6950.7361.0000.731-0.8500.6000.8070.8110.7980.8120.997
total_dpea0.9941.0000.9820.9800.9920.7311.000-0.9310.7260.9890.9670.9650.9780.588
interval-0.926-0.931-0.920-0.904-0.922-0.850-0.9311.000-0.668-0.931-0.918-0.906-0.9230.719
PEL_total0.8590.8570.8540.8570.8480.6000.726-0.6681.0000.8190.8600.8630.8360.156
PEL_business_day_mean0.9510.9550.9390.9440.9500.8070.989-0.9310.8191.0000.9440.9480.9670.654
PEL_saturday_mean0.9810.9770.9960.9910.9710.8110.967-0.9180.8600.9441.0000.9920.9650.743
PEL_sunday_mean0.9810.9810.9860.9950.9780.7980.965-0.9060.8630.9480.9921.0000.9710.706
PEL_max0.9700.9730.9620.9680.9800.8120.978-0.9230.8360.9670.9650.9711.0000.693
line0.5320.5340.5750.5710.5510.9970.5880.7190.1560.6540.7430.7060.6931.000

Missing values

2023-06-10T20:54:23.667360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-10T20:54:23.873912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-10T20:54:24.068588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datelinePTL_totalPTL_MDU (Business Days Mean)PTL_MSD (Saturdays Mean)PTL_MDO (Sundays Mean)PTL_MAX (Daily Max)stationstotal_dpeaintervalPEL_totalPEL_business_day_meanPEL_saturday_meanPEL_sunday_meanPEL_max
02018-08-01338679000.01440000.0884000.0504000.01483000.0NaNNaNNaN32413000.01211000.0724000.0418000.01248000.0
12018-08-01137775000.01420000.0832000.0446000.01466000.0NaNNaNNaN27733000.01048000.0598000.0307000.01086000.0
22018-08-0115516000.022000.0NaN2000.023000.0NaNNaNNaNNaNNaNNaNNaNNaN
32018-08-01218164000.0701000.0318000.0194000.0723000.0NaNNaNNaN14509000.0560000.0250000.0155000.0581000.0
42018-08-0157220000.0309000.0176000.085000.0322000.0NaNNaNNaNNaNNaNNaNNaNNaN
52018-08-01420844000.0797000.0397000.0229000.0827000.0NaNNaNNaNNaNNaNNaNNaNNaN
62018-09-01418080000.0800000.0411000.0111000.0841000.0NaNNaNNaNNaNNaNNaNNaNNaN
72018-09-0157632000.0323000.0182000.093000.0345000.0NaNNaNNaNNaNNaNNaNNaNNaN
82018-09-0115445000.023000.03000.02000.023000.0NaNNaNNaN243000.013000.01000.01000.013000.0
92018-09-01216246000.0705000.0321000.0202000.0733000.0NaNNaNNaN12946000.0562000.0254000.0162000.0582000.0
datelinePTL_totalPTL_MDU (Business Days Mean)PTL_MSD (Saturdays Mean)PTL_MDO (Sundays Mean)PTL_MAX (Daily Max)stationstotal_dpeaintervalPEL_totalPEL_business_day_meanPEL_saturday_meanPEL_sunday_meanPEL_max
3322023-03-01513455770.0503110.0305230.0165840.0535970.017.0503120.0NaNNaNNaNNaNNaNNaN
3332023-03-01417370640.0649070.0389010.0221480.0711990.011.0649070.0NaNNaNNaNNaNNaNNaN
3342023-03-01152876000.0120000.065000.035000.0128000.011.0120000.0184.01692000.072000.040000.021000.074000.0
3352023-03-01327409000.01067000.0676000.0387000.01096000.018.01066000.0119.022571000.0888000.0539000.0311000.0914000.0
3362023-04-01325011000.01066000.0627000.0371000.01157000.018.01064000.0119.020602000.0886000.0501000.0296000.0973000.0
3372023-04-01214544000.0649000.0299000.0189000.0671000.014.0651000.0136.010961000.0492000.0219000.0140000.0512000.0
3382023-04-01415070570.0661450.0338300.0201580.0701710.011.0661470.0NaNNaNNaNNaNNaNNaN
3392023-04-01511792220.0510810.0282420.0160710.0527250.017.0510800.0NaNNaNNaNNaNNaNNaN
3402023-04-01125545000.01091000.0639000.0375000.01130000.023.01092000.0119.018701000.0808000.0453000.0262000.0830000.0
3412023-04-01152827000.0124000.063000.037000.0127000.011.0123000.0184.01653000.072000.037000.022000.074000.0